Medical imaging is a well-established technique in the field of equipment for medical applications. Particularly, this technique is commonly exploited for the assessment of blood perfusion, which finds use in several diagnostic applications and especially in ultrasound analysis. For this purpose, an ultrasound contrast agent (UCA), for example, consisting of a suspension of phospholipid-stabilized gas-filled microbubbles, is administered to a patient. The contrast agent acts as an efficient ultrasound reflector, so that it can be easily detected by applying ultrasound waves and recording a resulting echo signal. As the contrast agent flows at the same velocity as the blood in the patient, its tracking provides information about the perfusion of the blood in a body part to be analyzed.
Typically, the flow of the contrast agent is monitored by acquiring a sequence of consecutive images representing the body part during the perfusion process. More in detail, the value of each pixel of the images indicates an intensity of the recorded echo signal for a corresponding portion of the body part. In this way, the sequence of images depicts the evolution over time of the echo signal throughout the body part. Therefore, the analysis of this sequence of images (for example, displayed on a monitor) provides a quantitative indication of the blood perfusion in the body part.
However, the quality of the images that are displayed is often quite poor. Indeed, the value of each pixel of the images exhibits large variations in time. Moreover, the inevitable presence of speckle grains in the images produces distracting patterns. The quality of the images is also adversely affected by any motion of the body part during acquisition. Another factor that hinders an effective analysis of the images is the background echo signals that are superimposed to the useful information. All of the above makes it very difficult to establish a correct assessment of the perfusion process; in any case, the results obtained are strongly dependent on the skill of an operator who acquires and/or analyzes the sequence of images.
Some attempts have been made to improve the quality of the images in specific applications. For example, U.S. Pat. No. 6,676,606, which is incorporated by reference, proposes a solution for facilitating the identification of tiny blood vessels in the body part. For this purpose, the images are processed to reduce the contribution of pixels that are the same from image to image (for example, associated with stationary tissues) and to make persistent the pixels that instead change from image to image (for example, due to a moving microbubble). This enhances the visualization of micro-vascular structures.
On the other hand, a quantitative assessment of the perfusion process is provided by parametric analysis techniques. In this case, the intensity of the echo signal that is recorded over time (for each single pixel or group of adjacent pixels) is fitted by a mathematical model function. The model function can then be used to calculate different perfusion parameters, which are indicative of corresponding morphological characteristics of the respective portion of the body part. This technique has been proposed for the first time in Wei, K., Jayaweera, A. R., Firoozan, S., Linka, A., Skyba, D. M., and Kaul, S., “Quantification of Myocardial Blood Flow With Ultrasound-Induced Destruction of Microbubbles Administered as a Constant Venous Infusion,” Circulation, vol. 97, 1998, which is incorporated by reference. For example, in a so-called destruction-replenishment technique (wherein the contrast agent is destroyed by a flash of sufficient energy, so as to observe its reperfusion following destruction), a commonly accepted model is defined by a mono-exponential function I(t) of the intensity of the (video) echo signal against the time, with a general form:I(t)=A·(1−e−βt)where A is a steady-state amplitude and β is a velocity term of the mono-exponential function (with the time origin taken at the instant immediately following the destruction flash). In this case the perfusion parameters are the values A and β; these values have commonly been interpreted as quantities proportional to a regional blood volume and a blood velocity, respectively, while the value Aβ has been interpreted as a quantity proportional to the flow.
Parametric imaging techniques are also commonly used for graphically representing the result of the above-described quantitative analysis. For this purpose, a parametric image is built by assigning the corresponding value of a selected perfusion parameter to each pixel. Typically, different ranges of values of the perfusion parameter are coded with corresponding colors; the pixel values so obtained are then overlaid on an original image. The parametric image shows the spatial distribution of the perfusion parameter throughout the body part under analysis; this facilitates the identification of possible portions of the body part that are abnormally perfused (for example, because of a pathological condition).
However, the parametric image simply provides a static representation of the values of the perfusion parameter. Therefore, it does not allow a direct visual perception of the perfusion process, which is normally provided by the playback of the original sequence of images.
A different approach is proposed in US Publication No. 2003/0114759, which is incorporated by reference. In this case, multiple single-phase sequences of images are built; each single-phase sequence is obtained by assembling all the images that were acquired at a corresponding phase of different cardiac cycles. For each single-phase sequence of images, the corresponding pixel values (or groups thereof) are fitted by a model function as above; a parametric image is again built by assigning the corresponding values of a selected perfusion parameter (calculated from the model function) to each pixel. The sequence of parametric images so obtained (for the different phases of the cardiac cycle) may be displayed in succession. The cited document also hints to the possibility of using the same technique for other organs that are not strongly related to the heart cycle (such as the liver, the kidney, a transplanted organ or a limb of the body). In this case, the above-described procedure is applied to different periods of the diagnostic process; for each period, a distinct parametric image is likewise generated from the corresponding sub-sequence of images (with these parametric images that may again be displayed in succession). In any case, the perfusion parameters are still formed using the values A, β and their combinations (such as A*β or A/β); alternatively, it is also possible to base the perfusion parameters on the error or variance of the corresponding model function.
The above-described solution provides some sort of indication of the perfusion changes at the different phases of the heart cycle (or more generally of the diagnostic process). Nevertheless, each parametric image is still based on fixed perfusion parameters representing the corresponding model function statically.